Predicting Dielectric Waveguides Characteristics Using Deep Learning
Elsheikh, Omar E.; Shaaban, Adel; Arafa, A.; Gad, Nasr; Yahya, Ashraf; Gomaa, Lotfy Rabeh; Swillam, M.;
Abstract
We propose an unsupervised deep learning model based on physics-informed neural network (PINNS) to find the effective refractive index of a slab waveguide. The model accuracy could reach 99% within a time range from 60 to 120 seconds for symmetric and anti-symmetric waveguide. The results show the success of the introduced method in solving fail cases of the compared methods.
Other data
| Title | Predicting Dielectric Waveguides Characteristics Using Deep Learning | Authors | Elsheikh, Omar E.; Shaaban, Adel; Arafa, A.; Gad, Nasr ; Yahya, Ashraf; Gomaa, Lotfy Rabeh; Swillam, M. | Keywords | deep learning;optical detector;waveguides | Issue Date | 1-Jan-2022 | Conference | 2022 Photonics North, PN 2022 | ISBN | 9781665453011 | DOI | 10.1109/PN56061.2022.9908369 | Scopus ID | 2-s2.0-85141217873 |
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| Predicting_Dielectric_Waveguides_Characteristics_Using_Deep_Learning.pdf | 817.58 kB | Adobe PDF | Request a copy |
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